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# Data Scientist / PostDoc - Machine Learning & Profile - Driven Enzyme Discovery

- **Company:** Bayer AG
- **Location:** Monheim am Rhein, Germany
- **Contract:** Permanent contract
- **Skills:** Computational Biology, Machine Learning, Deep Learning, Optimization Algorithms, Programming Languages
- **Published:** September 16, 2026
- **Apply:** https://career.bayer.ch/en/?search_result=false&8f_pid=562949978335405

## About the Role

* You hold a PhD in machine learning, computational biology, physics, mathematics, or a highly quantitative discipline.
 * You bring deep theoretical and practical expertise in advanced machine learning, specifically probabilistic modeling, optimization algorithms, and active learning strategies geared towards scientific discovery.
 * You have a solid grasp of protein chemistry and mutational effects, ensuring that ML-generated predictions are biologically plausible and translate into actionable discoveries for the wet lab.
 * You actively challenge the status quo, relentlessly pursuing methodological innovation to solve complex, noisy biological problems and uncover new mechanisms in novel ways.
 * You possess strong collaboration strategies, successfully orchestrating the "Closed Loop" process by seamlessly bridging algorithmic hypothesis generation, wet-lab execution, and model refinement.
 * You are proficient in modern programming languages and the standard ecosystems for deep learning and probabilistic modeling.
 * You communicate clearly in English, both verbally and in writing, and can distill complex probabilistic concepts and scientific discoveries into strategic insights for cross-functional teams and leadership.

## Description

* Develop and deploy active learning loops and probabilistic optimization strategies to explore vast, unknown sequence spaces and drive the discovery of highly informative variants for lab testing.
 * Build multi-objective optimization models capable of discovering entirely new enzymes that simultaneously meet complex performance profiles.
 * Implement predictive machine learning models, leveraging state-of-the-art protein representation learning to capture deep sequence-structure-function relationships and uncover novel biological insights.
 * Collaborate closely with scientific data experts to leverage complex knowledge graphs, and work with wet-lab scientists to evaluate model-generated hypotheses and interpret Design of Experiments (DoE) results.
 * Drive methodological innovation and translate highly complex probabilistic models and algorithmic discoveries into clear business impacts, risk assessments, and R&D strategies for executive leadership.